In magnetic resonance imaging, imaging speed is an important criterion in evaluating imaging method. Factors affecting the imaging speed include data collecting time and/or K-space fill-in time. A conventional data sampling method requires a full-size sampling of the K-space data before MRI reconstruction can be performed. MRI reconstruction with parallel sampling technique utilizes a coil array combination method to fill the un-sampled data and perform reconstruction with the completely filled K-space data. With such method, only a portion of the K-space data, rather than the whole K-space data, is required for sampling. As such, the data sampling time is decreased and the imaging speed is increased.
One of the commonly adopted parallel reconstruction methods is GRAPPA. FIG. 1 illustrates a conventional GRAPPA algorithm, where black solid dots denote the actually sampled K-space data, white hollow dots denote un-sampled data which needs to be filled in by parallel reconstruction method, and grey solid dots denote data which is sampled for the purpose of calculating parameters for coil array combination. According to GRAPPA algorithm, any hollow dot in the figure can be represented by linear addition of the adjacent black solid dots. This equals to the combination of data from a plurality of coils. The combination coefficient nij (coil i, position j, as shown in FIG. 1) can be determined from the black solid dots for best fitting the grey dots. After the coefficient is determined, the data represented by the other white hollow dots now can be renewed from data collected by coils multiplying combination coefficient. In certain situation, in the data acquired via the foregoing parallel sampling technique, there is a large portion of noise component. The quality of the image obtained from such data can be compromised by the noise.